• DocumentCode
    259010
  • Title

    A Genetic-Simulated Annealing Algorithm Based on PTS Technique for PAPR Reduction in OFDM System

  • Author

    Xia Wang ; Songhua He ; Tao Zhu

  • Author_Institution
    Dept. Coll. of Inf. Sci. & Eng., Hunan Univ., Changsha, China
  • fYear
    2014
  • fDate
    26-27 July 2014
  • Firstpage
    120
  • Lastpage
    124
  • Abstract
    In this paper, we propose a genetic-simulated annealing algorithm (GSAA) with partial transmit sequence (PTS) technique (GSAA-PTS) to reduce the peak-to-average power ratio (PAPR) for orthogonal frequency division multiplexing (OFDM) system. Genetic algorithm based PTS method (GA-PTS) is a novel and suboptimal PAPR reduction approach, which has lower computational load than original PTS method. However, researchers still focus on how to improve the PAPR performance. The proposed method GSAA-PTS takes the advantages of genetic algorithm and simulated annealing algorithm to search the best phase factors of PTS. In this method, we initialize the population firstly, and then selection, crossover and mutation are used to generate the new population, finally using the simulated annealing algorithm to update each chromosome in the population. The simulation results show that the better performance of PAPR have been achieved by adopting GSAA-PTS scheme.
  • Keywords
    OFDM modulation; genetic algorithms; simulated annealing; GA-PTS; GSAA-PTS; OFDM system; PAPR reduction; chromosome; genetic-simulated annealing algorithm; orthogonal frequency division multiplexing system; partial transmit sequence technique; peak-to-average power ratio reduction; Biological cells; Genetic algorithms; Partial transmit sequences; Peak to average power ratio; Sociology; Statistics; GSAA-PTS; Genetic Algorithm (GA); OFDM; PAPR; Partial Transmit Sequence (PTS); Simulated Annealing Algorithm (SAA);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Applications and Communications (SCAC), 2014 IEEE Symposium on
  • Conference_Location
    Weihai
  • Type

    conf

  • DOI
    10.1109/SCAC.2014.32
  • Filename
    6913180